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2018 | OriginalPaper | Buchkapitel

An Algorithm Design of Kansei Recommender System

verfasst von : Pei-Chun Lin, Nureize Arbaiy

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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Abstract

We propose an algorithm design for a Recommender System based on a Kansei model in this paper, we called this algorithm as Kansei Recommender System (hereafter, we denoted as KRS algorithm). The purpose of KRS algorithm is to support designers to pre-know the appearance feeling (Kansei) of products from consumers. To complete this algorithm, we divide the algorithm design into three parts: (1) Extract Kansei factors and evaluation factors from consumers’ shopping items. (2) Determine a Kansei model for KRS algorithm. (3) Making decision by using KRS algorithm. We also give a concept map of paradigm by using KRS algorithm. In conclusion, we remain the future work to implement the KRS algorithm in real case studies with different fields of enterprises.

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Titel: An Algorithm Design of Kansei Recommender System
verfasst von: Pei-Chun Lin
Nureize Arbaiy
Verlag: Springer International Publishing
Buch: Recent Advances on Soft Computing and Data Mining
Print ISBN: 978-3-319-72549-9

Electronic ISBN: 978-3-319-72550-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-72550-5_12

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